CN102540252B - High-precision median stacking method on basis of cross-correlation - Google Patents
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Abstract
The invention provides a high-precision median stacking method on the basis of cross-correlation. The method comprises the following steps: (1) stacking and filtering a trace gather subjected to dynamic correction treatment to obtain a model trace; (2) calculating a cross-correlation time difference of the model trace and one seismic trace of the current trace gather and correcting the time difference; (3) calculating a cross-correlation time difference of the model trace and a next seismic trace of the current gather and correcting the time difference until the time difference correction of all the seismic traces of the current gather is completed; (4) removing abnormal values in sampling points which take part in stacking, calculating an arithmetic mean value on the residual sampling points and removing the sampling point values of which symbols are opposite to those of the arithmetic mean value; (5) generating a weight of each sampling point which takes part in stacking in the residual sampling points; (6) unitizing the weight of each sampling point of the current gather, which takes part in stacking; (7) according to the unitized weights, completing the weighted stacking of each sampling point; and (8) repeatedly executing the steps (1) to (7) and carrying out weighted stacking of each sampling point of a next gather until the weighted stacking of each sampling point of all gathers is completed.
Description
Technical field
The present invention relates to petroleum gas acquiring seismic exploration data field, specifically, relate to a kind of high precision intermediate value stacking method based on simple crosscorrelation, the field seismic data that is mainly used in petroleum gas seismic prospecting is processed explanation.
Background technology
Complex area stacking image method research is the medium-term and long-term existence of oil earth rock gas physical prospecting in the frontier nature research topic solving not yet completely at present, and research has at present obtained a lot of theories and practical application achievement.Current research concentrates on following two aspects mostly:
1, how research obtains the more preferably method of normal moveout correction road collection
In seismic data processing practically, the lineups of the same reflection point of normal moveout correction road collection generally cannot strictly be smoothed, often by considering that coefficient of anisotropy or high-order term realize the road collection of offset distance far away and even up, but in seismic data processing practically, because the factor on earth's surface cannot be eliminated completely, and the horizontal change of shallow-layer speed makes nearly offset distance or middle offset distance still can have the phenomenon that cannot smooth conventionally, these means of shaking impenetrably propagation realize smoothing, so by tectonic model road, the correlation theory of utilizing mathematics time realize the processing of residual static correction in window, obtain more desirable road collection.
Above-mentioned research mainly comprises two parts content: the one, improve the computational accuracy of stack velocity, main method is to utilize anisotropy velocity analysis to obtain stack velocity and anisotropic parameter etc., thereby realize normal moveout correction road collection in, offset distance far away correctly playbacks; The 2nd, improve the precision that normal moveout correction is calculated, as utilize high-order normal moveout correction method etc. realize moving correcting track collection in, offset distance far away correctly playbacks.Because the influence factor of complex structural area seismic velocity is a lot, although said method has improved the precision of velocity analysis to a certain extent, can not ensure to obtain accurate stacking velocity field.
2, utilize stacking method more flexibly to realize the high precision imaging to road collection
These class methods are main study hotspots at present, have obtained some achievements in research, have obtained application comparatively widely.For example, but the research of this respect mainly still respectively lays particular emphasis on some aspects, and, Coherent addition is just considered the correlation computations of collection, the rejecting to exceptional value is just considered in smart stacking.These methods can only be exported the data after stack, cannot take into account in many aspects simultaneously, therefore, conventionally have to accept or reject to some extent, will inevitably affect like this precision of imaging.Even if said method adopts weighting processing, its weights calculate also very complicated, apply inaccurately, and do not consider that some seismic trace that participates in stack should reject, and can more be conducive to the stacking image of section after rejecting.
Although anisotropy normal moveout correction and high-order normal moveout correction method have advantages of that precision is high, they are not suitable for complex structure imaging, and existing stacking method function singleness, has little significance for complex area practical operation.
Summary of the invention
The problem existing for prior art, the invention provides a kind of high precision intermediate value stacking method based on simple crosscorrelation, described method comprises: (1) superposes to normal moveout correction road after treatment collection, and the data to stack are carried out filtering, the model trace using filtered data as the seismic trace correction calculation for current road collection; (2) when given in window, the simple crosscorrelation time difference between the seismic trace that the participation of computation model road and current road collection is calculated, and the seismic trace that described participation is calculated carries out TEC time error correction; (3) the simple crosscorrelation time difference between next seismic trace that the participation of computation model road and current road collection is calculated, and next seismic trace of described participation calculating is carried out to TEC time error correction, until complete the TEC time error correction of all seismic traces of the participation calculating of current road collection; (4) reject the exceptional value in the sampling point that participates in stack, and remaining sampling point is asked to arithmetic mean, reject the sample value contrary with arithmetic mean symbol; (5) in remaining sampling point, produce the weights that participate in each sampling point superposeing; (6) weights of each sampling point of the participation stack to current road collection are normalized; (7), according to the weights after the normalization of calculating, complete the weighted stacking of each sampling point; (8) repeated execution of steps (1), to step (7), is descended the weighted stacking of each sampling point of collection together, until complete the weighted stacking of each sampling point of all roads collection, to realize the accurately image of underground complex structure.
In step (1), normal moveout correction road after treatment collection can be common midpoint gather.
In step (5), can adopt the anti-distance weighted mode of sampling point amplitude and average, in remaining sampling point, produce the weights of each sampling point that participates in stack.
Can calculate according to formula below the weights of each sampling point:
Wherein, the arithmetic mean that A is sampling point, x
ifor current sample value, n is index, the sequence number that i is each sampling point.
In step (4), the exceptional value in sampling point can be the extreme value in sampling point.
High precision intermediate value stacking method based on simple crosscorrelation according to the present invention is processed and imaging applicable to the structure of Complex Mountain, there is the features such as easy, counting yield is high and imaging effect is good of calculating, in Complex Mountain seismic data is processed, have broad application prospects.
Brief description of the drawings
In conjunction with the drawings, from the description of the following examples, the present invention these and/or other side and advantage will become clear, and are easier to understand, wherein:
Fig. 1 is according to the process flow diagram of the high precision intermediate value stacking method based on simple crosscorrelation of the present invention.
Embodiment
In the present invention, normal moveout correction common midpoint gather after treatment is superposeed and obtains collection data, then these data are carried out to filtering processing, the model trace using the data that obtain as the seismic trace correction calculation for current road collection.By calculating the simple crosscorrelation time difference between the seismic trace (can be described as and calculate road) that the participation of this model trace and current road collection calculates in window when given, the seismic trace that participates in calculating is carried out to TEC time error correction.Equally, other seismic trace that the participation of current road collection is calculated also adopts this given time window, and the calculating simple crosscorrelation time difference is also proofreaied and correct the simple crosscorrelation time difference, thereby completes the TEC time error correction of all seismic traces that the participation of current road collection is calculated.
Then, adopt the mode of smart stacking, reject the exceptional value in the sampling point that participates in stack, and remaining sampling point is asked to arithmetic mean, reject the sample value contrary with mean value symbol.In remaining sampling point, (employing sampling point amplitude and average) anti-distance weighted mode, produces the weights that participate in each sampling point superposeing, and finally realizes weighted stacking.
Can descend the TEC time error correction of collection together by similar mode, until complete the TEC time error correction of all roads collection, thus realize the more accurate imaging of underground complex structure.
Below, describe the high precision intermediate value stacking method based on simple crosscorrelation in detail with reference to Fig. 1.Fig. 1 is according to the process flow diagram of the high precision intermediate value stacking method based on simple crosscorrelation of the present invention.
With reference to Fig. 1, in step 101, normal moveout correction road after treatment collection is superposeed, and the data of stack are carried out to filtering, the model trace using filtered data as the seismic trace correction calculation for current road collection.Preferably, normal moveout correction road after treatment collection is common midpoint gather.
In step 102, when given in window, the simple crosscorrelation time difference between the seismic trace that the participation of computation model road and current road collection is calculated, and the seismic trace that described participation is calculated carries out TEC time error correction.
In step 103, the simple crosscorrelation time difference between next seismic trace that the participation of computation model road and current road collection is calculated, and next seismic trace that described participation is calculated carries out TEC time error correction.
In step 104, determine whether the TEC time error correction of all seismic traces of the participation calculating of current road collection.
If do not complete the TEC time error correction of all seismic traces of the participation calculating of current road collection, turn back to step 103, proceed the TEC time error correction of other seismic trace of the participation calculating of current road collection.
The TEC time error correction of all seismic traces that the participation of current road collection is calculated if be over, in step 105, rejects the exceptional value in the sampling point that participates in stack, and remaining sampling point is asked to arithmetic mean, rejects the sample value contrary with arithmetic mean symbol.Preferably, the exceptional value in sampling point can be the extreme value in sampling point.
In step 106, in remaining sampling point, produce the weights of each sampling point that participates in stack.Preferably, can adopt the anti-distance weighted mode of sampling point amplitude and average, in remaining sampling point, produce the weights of each sampling point that participates in stack.
The weights computing formula of each sampling point is as follows:
Wherein, the arithmetic mean that A is sampling point, x
ifor current sample value, n is index, the sequence number that i is each sampling point.
In step 107, the weights of each sampling point to the participation stack with collection (current road collection) are normalized.
In step 108, according to the weights after the normalization of calculating, complete the weighted stacking of each sampling point.
In step 109, repeated execution of steps 101-108, descends the weighted stacking of each sampling point of collection together.
In step 110, determine whether the weighted stacking of each sampling point of all roads collection.
If do not complete the weighted stacking of each sampling point of all roads collection, turn back to step 101, proceed the weighted stacking of each sampling point of other road collection.
If completed the weighted stacking of each sampling point of all roads collection, the method stops, and finally realizes the weighted stacking of each sampling point of all roads collection, to realize the accurately image of underground complex structure.
For example, for a series of sample value, if each sample value equals the mean value of these a series of sample value, the superposition value of these sample value just equals its sample value.For example, if there are four sample value: 5,5,5,5, its superposition value M=(5+5+5+5)/4=5; For example, if wherein certain sample value is exceptional value (four sample value is respectively 5,5,9,5), its superposition value M '=(5+5+9+5)/4=6, therefore can not reflect original actual value M=5, now calculate the weights (wherein n=2) of each sampling point according to formula described above:
The weights of each sampling point calculating are respectively 1,1,1/9,1, its normalized value is respectively 9/28,9/28,1/28,9/28, therefore the weighted stacking value of these sampling points is: 5 × 9/28+5 × 9/28+9 × 1/28+5 × 9/28=5.142, more approaches actual value 5 than M '=6 like this.Therefore,, even also there are indivedual exceptional values after by foregoing step 105 rejecting abnormalities value, also can, by the interference of the weighted stacking rejecting abnormalities value of each sampling point, be convenient to realize the more accurate imaging of underground complex structure.
High precision intermediate value stacking method based on simple crosscorrelation according to the present invention can be had and had the following advantages:
1, adopt directly stack production model road, result of calculation is more accurate, and counting yield also improves greatly;
2, utilize model trace and calculate the calculating of the simple crosscorrelation time difference and the mode of proofreading and correct between road, computational accuracy is also largely increased.
High precision intermediate value stacking method based on simple crosscorrelation according to the present invention is specially adapted to the structure of Complex Mountain and processes and imaging, there is the features such as easy, counting yield is high and imaging effect is good of calculating, in Complex Mountain seismic data is processed, have broad application prospects.
Although the present invention is described in detail and shows with reference to its exemplary embodiment, but will be understood by those skilled in the art that, in the case of not departing from the spirit and scope of the present invention that are defined by the claims, can carry out to it various changes of form and details.
Claims (3)
1. the high precision intermediate value stacking method based on simple crosscorrelation, comprising:
(1) normal moveout correction road after treatment collection is superposeed, and the data of stack are carried out to filtering, the model trace using filtered data as the seismic trace correction calculation for current road collection;
(2) when given in window, the simple crosscorrelation time difference between the seismic trace that the participation of computation model road and current road collection is calculated, and the seismic trace that described participation is calculated carries out TEC time error correction;
(3) the simple crosscorrelation time difference between next seismic trace that the participation of computation model road and current road collection is calculated, and next seismic trace of described participation calculating is carried out to TEC time error correction, until complete the TEC time error correction of all seismic traces of the participation calculating of current road collection;
(4) reject the exceptional value in the sampling point that participates in stack, and remaining sampling point is asked to arithmetic mean, reject the sample value contrary with arithmetic mean symbol;
(5) in remaining sampling point, produce the weights that participate in each sampling point superposeing;
(6) weights of each sampling point of the participation stack to current road collection are normalized;
(7), according to the weights after the normalization of calculating, complete the weighted stacking of each sampling point;
(8) repeated execution of steps (1), to step (7), is descended the weighted stacking of each sampling point of collection together, until complete the weighted stacking of each sampling point of all roads collection, to realize the accurately image of underground complex structure,
Wherein, in step (5), adopt the anti-distance weighted mode of sampling point amplitude and average, in remaining sampling point, produce the weights of each sampling point that participates in stack,
Wherein, calculate the weights of each sampling point according to formula below:
Wherein, the arithmetic mean that A is sampling point, x
ifor current sample value, n is index, the sequence number that i is each sampling point.
2. high precision intermediate value stacking method according to claim 1, wherein, in step (1), normal moveout correction road after treatment collection is common midpoint gather.
3. high precision intermediate value stacking method according to claim 2, wherein, in step (4), the exceptional value in sampling point is the extreme value in sampling point.
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| CN104155688A (en) * | 2014-08-13 | 2014-11-19 | 中国石油集团川庆钻探工程有限公司地球物理勘探公司 | High precision weighted stack method |
| CN104181588B (en) * | 2014-08-15 | 2016-08-31 | 中国石油集团川庆钻探工程有限公司地球物理勘探公司 | A kind of method building high-quality model trace |
| CN105425295B (en) * | 2015-12-18 | 2017-11-14 | 中国石油集团东方地球物理勘探有限责任公司 | Geological data is superimposed weights acquisition methods and stage by stage stacking method stage by stage |
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| CN114002741B (en) * | 2020-07-28 | 2024-01-30 | 中国石油天然气股份有限公司 | Pre-stack depth migration method and device and computer readable storage medium |
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